VP - AI Engineering Lead

Job Description

Lead the design and deployment of Generative AI and agentic systems that transform customer servicing, automate complex workflows, and significantly reduce operational demand. Shape how LLM-driven capabilities are embedded into production platforms, delivering measurable impact across millions of customer interactions.

Required Skills

• Proven experience designing and deploying production-grade Generative AI systems, including LLM-based applications (RAG, prompt orchestration, tool/agent integration)
• Hands-on expertise in building scalable AI/ML platforms in cloud environments (AWS or Azure), including model deployment, monitoring, and lifecycle management (MLOps)
• Deep experience architecting end-to-end AI pipelines (data ingestion, feature engineering, real-time/streaming architectures with Kafka, Kinesis, Spark, and low-latency inference systems)
• Solid proficiency in Python-based AI/ML development (PyTorch, TensorFlow, scikit-learn) and GenAI-specific tooling/frameworks
• Demonstrated ability to lead engineering teams and deliver complex AI programs, partnering across product, architecture, and operations

Highly Valued Skills

• Experience with LLM platforms (AWS Bedrock, Azure OpenAI), including evaluation, fine-tuning, safety guardrails, and cost/performance optimization
• Familiarity with agentic orchestration and multi-step reasoning systems for customer-facing or enterprise automation (e.g., virtual assistants, workflow automation)
• Practical experience with containerization (Docker, Kubernetes) for GenAI and ML workloads
• Good grounding in Responsible AI, model risk management, and governance (explainability, bias mitigation, auditability in regulated environments)
• Ability to translate ambiguous business problems into AI-driven solutions with focus on measurable outcomes (cost efficiency, CX improvement, automation at scale)

Benefits Eligible for medical, dental, vision coverage, 401(k), life insurance, paid leave, and an incentive award.

Purpose of the Role Use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, informing strategic decision-making, improving operational efficiency, and driving innovation.

Accountabilities

• Identification, collection, extraction of data from various sources
• Data cleaning, wrangling, and transformation for quality and suitability
• Development and maintenance of efficient data pipelines for automated data acquisition and processing
• Design and conduct statistical and machine learning models to analyze patterns, trends, and relationships
• Development and implementation of predictive models to forecast future outcomes and identify risks/opportunities
• Collaborate with business stakeholders to add value from data through Data Science

Vice President Expectations

• Contribute to strategy, drive requirements, and make recommendations for change
• Plan resources, budgets, and policies; manage processes; deliver continuous improvements
• If managing a team: define jobs/responsibilities, plan future needs, counsel on performance, contribute to pay decisions
• If individual contributor: subject matter expert guiding technical direction, leading multi-year assignments, training/coaching less experienced specialists
• Advise key stakeholders on functional and cross-functional areas of impact
• Manage risks through assessment in support of control and governance agenda
• Demonstrate leadership and accountability for managing risk and strengthening controls
• Collaborate with other areas of work to stay aligned with business activity and strategies
• Create solutions based on sophisticated analytical thought and in-depth analysis
• Build and maintain trusting relationships with internal and external stakeholders
• Demonstrate Barclays Values (Respect, Integrity, Service, Excellence, Stewardship) and Mindset (Empower, Challenge, Drive)